1 / 29

Bayesian Subgroup Analysis

Bayesian Subgroup Analysis. Gene Pennello, Ph.D. Division of Biostatistics, CDRH, FDA Disclaimer: No official support or endorsement of this presentation by the Food & Drug Administration is intended or should be inferred. FIW 2006 September 28, 2006. Outline.

anila
Download Presentation

Bayesian Subgroup Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Bayesian Subgroup Analysis Gene Pennello, Ph.D. Division of Biostatistics, CDRH, FDA Disclaimer: No official support or endorsement of this presentation by the Food & Drug Administration is intended or should be inferred. FIW 2006 September 28, 2006

  2. Outline Frequentist Approaches Bayesian Hierarchical Model Approach Bayesian Critical Boundaries Directional Error Rate Power Summary

  3. Frequentist Approaches Strong control of FWE Weak control of FWE Gatekeeper: test subgroups (controlling FWE) only if overall effect is significant Confirmatory Study: confirm with a new study in which only patients in the subgroup are enrolled.

  4. Concerns with Frequentist Approaches Limited power of FWE procedures Powerlessness of gatekeeper if overall effect is insignificant Discourages multiple hypothesis testing, thereby impeding progress. Confirmation of findings, one at a time, impedes progress.

  5. “No aphorism is more frequently repeated in connection with field trials, than that we must ask Nature few questions, or, ideally, one question at a time. The writer is convinced that this view is wholly mistaken. Nature, he suggests, will best respond to a logical and carefully thought out questionnaire …” Fisher RA, 1926, The arrangement of field experiments, Journal of the Ministry of Agriculture, 33, 503-513.

  6. A Bayesian Approach Adjust subgroup inference for its consistency with related results. Choices Build prior on subgroup relationships. Invoke relatedness by modeling a priori exchangeability of effects.

  7. Prior Exchangeability Model Subgroups: Labels do not inform on magnitude or direction of main subgroup effects. Treatments: Labels do not inform for main treatment effects. Subgroup by Treatment Interactions: Labels do not inform for treatment effects within subgroups.

  8. Prior Exchangeability Model Exchangeability modeled with random effects models. Key Result: Result for a subgroup is related to results in other subgroups because effects are iid draws from random effect distribution.

  9. Bayesian Two-Way Normal Random Effects Model

  10. Bayesian Two-Way Normal Random Effects Model Note: In prior distribution, Pr(zero effect) = 0 That is, only directional (Type III) errors can be made here.

  11. Known Variances Inference Subgroup Problem: Posterior Note: In prior distribution, Pr(zero effect) = 0 That is, only directional (Type III) errors can be made here.

  12. Bayes Decision Rule Declare difference > 0 if Let Note: In prior distribution, Pr(zero effect) = 0 That is, only directional (Type III) errors can be made here.

  13. if Bayes Critical z Value Linear dependence on standardized marginal treatment effect ↑ with ↓interaction (↑ ) ↓with ↑ # subgroups b.

  14. Full Interaction Case: Critical z value ↑ with ↓ true F ratio measuring heterogeneity of interaction effects. Bayes Critical z Value

  15. No Interaction Case: Critical z value Power can be > than for unadjusted 5% level z test for subgroup if true F ratio measuring heterogeneity of treatment effects is large. Bayes Critical z Value

  16. Full Bayes Critical t Boundaries

  17. Directional Error Control Directional FDR controlled at A under 0-1-A loss function for correct decision, incorrect decision, and no decision (Lewis and Thayer, 2004). Weak control of FW directional error rate, loosely speaking, because of dependence on F ratio for interaction.

  18. Comparisons of Sample Size to Achieve Same Power ULSD = 5% level unadjusted z test Bonf = Bonferonni 5% level z test HM = EB hierarchical model test

  19. EX. Beta-blocker for Hypertension Losartan versus atenolol randomized trial Endpoint: composite of Stroke/ MI/ CV Death N=9193 losartan (4605), atenolol (4588) # Events losartan (508), atenolol (588) 80% European Caucasians 55-80 years old. http://www.fda.gov/cder/foi/label/2003/020386s032lbl.pdf

  20. EX. Beta-blocker for Hypertension Cox Analysis N logHR SE HR (95% CI) p val Overall9193 .87 ( .77, .98) 0.021 Race SubgroupsNon-Black 8660 -.19 .06 .83 ( .73, .94) 0.003Black 533 .51 .24 1.67 (1.04,2.66) 0.033 Is Finding Among Blacks Real or a Directional Error?

  21. EX. Beta-blocker for Hypertension Bayesian HM Analysis logHR se/sd HR (95%CI) p val Pr>0non-blackfrequentist -.19 .06 0.83 ( .73 .94) 0.003 0.001Bayesian -.18 .06 0.84 ( .74, .95) 0.003 blackfrequentist .51 .24 1.67 (1.04, 2.67) 0.033 0.983Bayesian .38 .27 1.47 (0.87, 2.44) 0.914Bayesian analysis cast doubt on finding, but is predicated on not expecting a smaller effect in blacks a priori.

  22. Suggested Strategy Planned subgroup analysis Bayesian adjustment using above HM or similar model Pennello,1997, JASASimon, 2002, Stat. Med. Dixon and Simon, 1991, Biometrics

  23. Suggested Strategy Unplanned subgroup analysis Ask for confirmatory trial of subgroup. Posterior for treatment effect in the subgroup given by HM is prior for confirmatory trial. Prior information could reduce size of confirmatory trial.

  24. Summary Bayesian approach presented here considers trial as a whole, adjusts for consistency in finding over subgroups. Error rate is not rigidly pre-assigned Can vary from conservative to liberal depending on interaction F ratio and marginal treatment effect. Power gain can be substantial.Control for directional error rate is made only when warranted.

  25. References Dixon DO and Simon R (1991), Bayesian subset analysis, Biometrics, 47, 871-881. Lewis C and Thayer DT (2004), A loss function related to the FDR for random effects multiple comparisons, Journal of Statistical Planning and Inference125, 49-58. Pennello GA (1997), The k-ratio multiple comparisons Bayes rule for the balanced two-way design, J. Amer. Stat. Assoc.,92, 675-684 Simon R (2002), Bayesian subset analysis: appliation to studying treatment-by-gender interactions, Statist. Med., 21, 2909-2916. Sleight P (2000), Subgroup analyses in clinical trials: fun to look at but don’t believe them!, Curr Control Trials Cardiovasc Med, 1, 25-27.

  26. Other Notable References Berry DA, 1990, Subgroup Analysis (correspondence) Biometrics, 46, 1227-1230. Gonen M, Westfall P, Johnson WO (2003), Bayesian multiple testing for two-sample multivariate endpoints, Biometrics, 59, 76-82. Westfall PH, Johnson WO, and Utts JM (1997), A Bayesian perspective on the Bonferroni adjustment, 84, 419-427

More Related